1 강현, "VGG-based BAPL Score Classification of 18F-Florbetaben Amyloid Brain PET" 대한의생명과학회 24 (24): 418-425, 2018
2 Segovia F, "Using CT data to improve the quantitative analysis of 18F-FBB PET neuroimages" 10 : 158-, 2018
3 Moćkus J, "The Application of Bayesian Methods for Seeking the Extremum: Toward global optimization 2" Elsevier 117-, 1978
4 Illán IA, "The Alzheimer's Disease Neuroimaging Initiative" 181 : 903-916, 2011
5 Piramal Imaging Limited, "Summary of product characteristics" Piramal Imaging Limited 2014
6 Haass C, "Soluble protein oligomers in neurodegeneration: lessons from the Alzheimer's amyloid β-peptide" 8 : 101-, 2007
7 Lopresti BJ, "Simplified quantification of Pittsburgh Compound B amyloid imaging PET studies: a comparative analysis" 46 : 1959-1972, 2005
8 Lundeen TF, "Signs and artifacts in Amyloid PET" 38 : 2123-2133, 2018
9 Pedregosa F, "Scikit-learn: machine learning in python" 12 : 2825-2830,
10 Bergstra J, "Random search for hyper-parameter optimization" 13 : 281-305, 2012
1 강현, "VGG-based BAPL Score Classification of 18F-Florbetaben Amyloid Brain PET" 대한의생명과학회 24 (24): 418-425, 2018
2 Segovia F, "Using CT data to improve the quantitative analysis of 18F-FBB PET neuroimages" 10 : 158-, 2018
3 Moćkus J, "The Application of Bayesian Methods for Seeking the Extremum: Toward global optimization 2" Elsevier 117-, 1978
4 Illán IA, "The Alzheimer's Disease Neuroimaging Initiative" 181 : 903-916, 2011
5 Piramal Imaging Limited, "Summary of product characteristics" Piramal Imaging Limited 2014
6 Haass C, "Soluble protein oligomers in neurodegeneration: lessons from the Alzheimer's amyloid β-peptide" 8 : 101-, 2007
7 Lopresti BJ, "Simplified quantification of Pittsburgh Compound B amyloid imaging PET studies: a comparative analysis" 46 : 1959-1972, 2005
8 Lundeen TF, "Signs and artifacts in Amyloid PET" 38 : 2123-2133, 2018
9 Pedregosa F, "Scikit-learn: machine learning in python" 12 : 2825-2830,
10 Bergstra J, "Random search for hyper-parameter optimization" 13 : 281-305, 2012
11 Platt J, "Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods" 10 : 61-74, 1999
12 Oh IS, "Pattern recognition" Kyobobook 137-173, 2008
13 Bullich S, "Optimized classification of 18F-Florbetaben PET scans as positive and negative using an SUVR quantitative approach and comparison to visual assessment" 15 : 325-332, 2017
14 Tamil Iniyan Gunasekaran, "MicroRNAs as Novel Biomarkers for the Diagnosis of Alzheimer's Disease and Modern Advancements in the Treatment" 대한의생명과학회 21 (21): 1-8, 2015
15 Brucher N, "Measurement of inter- and intra-observer variability in the routine clinical interpretation of brain 18-FDG PET-CT" 29 : 233-239, 2015
16 Snoek J, "In Advances in Neural Information Processing System" 2951-2959, 2012
17 Seibyl J, "Impact of training method on the robustness of the visual assessment of 18F-Florbetaben PET scan: results from a phase-3 study" 57 : 900-906, 2016
18 Gonçalves AB, "Feature extraction and machine learning for the classification of Brazilian savannah pollen grains" 11 : e0157044-, 2016
19 Chaves R, "FDG and PIB biomarker PET analysis for the Alzheimer's disease detection using Association Rules" s2576-s2579, 2012
20 Gulshan V, "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs" 316 : 2402-2410, 2016
21 Lakhani P, "Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks" 284 : 574-582, 2017
22 Taylor JC, "Comparison of machine learning and semiquantification algorithms for (I123) FP-CIT classification: the beginning of the end for semi-quantification?" 4 : 29-, 2017
23 DeLong ER, "Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach" 44 : 837-845, 1988
24 Barthel H, "Cerebral amyloid-β PET with florbetaben (18F) in patients with Alzheimer's disease and healthy controls: a multicenter phase 2 diagnostic study" 10 : 424-435, 2011
25 Blanc-Durand P, "Cerebral 18F-FDG PET in macrophagic myofasciitis: An individual SVM-based approach" 12 : e0181152-, 2017
26 Xue DX, "CNN-SVM for microvascular morphological type recognition with data augmentation" 36 : 755-764, 2016
27 Varma S, "Bias in error estimation when using crossvalidation for model selection" 7 : 91-, 2006
28 Choi WH, "Automated quantification of amyloid positron emission tomography: a comparison of PMOD and MIMNEURO" 30 : 682-689, 2016
29 Zhang Y, "A hybrid method for MRI brain image classification" 38 : 10049-10053, 2011
30 Sherman M, "A comparison between bootstrap methods and generalized estimating equations for correlate outcomes in generalized linear models" 26 : 901-925, 1997
31 Vapnik VN, "10.5 Support Vector Machine: Statistical Learning Theory" Wiley-Interscience 421-441, 1998